Table 1

Potential Predictor Variables Evaluated in 28 Therapy-Specific Logistic Regression Models

Dependent Variables for Logistic Regressions


Potential Predictor Variable

High Knowledge of Therapy*

Prior Use of Therapy*

Prior Use of Therapy for Back Pain*

High Expectations of Success of Therapy*

Likelihood of Trying Therapy at No Cost*

Likelihood of Trying Therapy for $10 Co-pay**


Geographic location (Boston vs. Seattle)

X

X

X

X

X

X

Age (65+ vs. < 65)

X

X

X

X

X

X

Gender (female vs. male)

X

X

X

X

X

X

Race (white, non-white)

X

X

X

X

X

X

Education (no college vs. some college)

X

X

X

X

X

X

≥ 5 years since first back pain

X

X

X

≥ 90 days of LBP in last 6 mo.

X

X

X

High symptom bothersomeness (7 – 10) on a 0 – 10 scale

X

X

X

High knowledge of therapy (4 or 5) on a 1 – 5 scale

X

X

X

Prior use of therapy

X

X

X

Prior use of therapy for back pain

X

X

X

High expectations of therapy (7 – 10) on a 0 – 10 scale

X

X

Medication usage in past week

X

X

Prior harm from therapy

X

X


* Separate models were done for each of the five therapies (acupuncture, chiropractic, massage, meditation, t'ai chi) ** Separate models were done for acupuncture, chiropractic, and massage. An X indicates that a particular potential predictor variable was evaluated in a model with the specific dependent variable.

Sherman et al. BMC Complementary and Alternative Medicine 2004 4:9   doi:10.1186/1472-6882-4-9

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